Welcome to the InfoSci Platform
Reference Hub2
An Ensemble Voted Feature Selection Technique for Predictive Modeling of Malwares of Android

An Ensemble Voted Feature Selection Technique for Predictive Modeling of Malwares of Android

Abhishek Bhattacharya, Radha Tamal Goswami, Kuntal Mukherjee, Nhu Gia Nguyen
Copyright: © 2019 |Volume: 10 |Issue: 2 |Pages: 24
ISSN: 1947-8186|EISSN: 1947-8194|EISBN13: 9781522566618|DOI: 10.4018/IJISMD.2019040103
Cite Article Cite Article

MLA

Bhattacharya, Abhishek, et al. "An Ensemble Voted Feature Selection Technique for Predictive Modeling of Malwares of Android." IJISMD vol.10, no.2 2019: pp.46-69. http://doi.org/10.4018/IJISMD.2019040103

APA

Bhattacharya, A., Goswami, R. T., Mukherjee, K., & Nguyen, N. G. (2019). An Ensemble Voted Feature Selection Technique for Predictive Modeling of Malwares of Android. International Journal of Information System Modeling and Design (IJISMD), 10(2), 46-69. http://doi.org/10.4018/IJISMD.2019040103

Chicago

Bhattacharya, Abhishek, et al. "An Ensemble Voted Feature Selection Technique for Predictive Modeling of Malwares of Android," International Journal of Information System Modeling and Design (IJISMD) 10, no.2: 46-69. http://doi.org/10.4018/IJISMD.2019040103

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

Each Android application requires accumulations of permissions in installation time and they are considered as the features which can be utilized in permission-based identification of Android malwares. Recently, ensemble feature selection techniques have received increasing attention over conventional techniques in different applications. In this work, a cluster based voted ensemble voted feature selection technique combining five base wrapper approaches of R libraries is projected for identifying most prominent set of features in the predictive modeling of Android malwares. The proposed method preserves both the desirable features of an ensemble feature selector, accuracy and diversity. Moreover, in this work, five different data partitioning ratios are considered and the impact of those ratios on predictive model are measured using coefficient of determination (r-square) and root mean square error. The proposed strategy has created significant better outcome in term of the number of selected features and classification accuracy.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.